Single-channel EOG artifact removal using fixed frequency EWT and GMETV filter.

Portable electroencephalogram (EEG) systems are increasingly used in healthcare due to their user-friendly and wearable design. However, accurate diagnosis can be hindered by electrooculogram (EOG) artifacts-low-frequency, high-amplitude signals caused by eye blinks. These artifacts are especially problematic in single-channel (SCL) EEG systems, necessitating robust artifact removal techniques. This study proposes an automated method for eliminating EOG artifacts from EEG signals using a Fixed Frequency Empirical Wavelet Transform (FF-EWT) integrated with a finely tuned Generalized Moreau Envelope Total Variation (GMETV) filter. The approach effectively separates artifact sources from the single-channel EEG by identifying contaminated components at the decomposition stage using kurtosis (KS), dispersion entropy (DisEn), and power spectral density (PSD) metrics. These components are then removed using the GMETV filter. The method was validated on both synthetic and real EEG datasets, demonstrating its capability to suppress EOG artifacts while preserving essential low-frequency EEG information. Performance evaluation revealed substantial improvements using the FF-EWT+GMETV technique, with lower Relative Root Mean Square Error (RRMSE) and higher Correlation Coefficient (CC) on synthetic data, and improved Signal-to-Artifact Ratio (SAR) and Mean Absolute Error (MAE) on real EEG recordings. This advancement offers strong potential for brain signal analysis, serving as an effective preprocessing tool in both clinical and research settings.